Development Evolution 9. Phenotypic Plasticity Phenotypic Plasticity

Total Page：16

File Type：pdf, Size：1020Kb

Development Evolution 9. Phenotypic Plasticity Phenotypic Plasticity

Development Evolution 9. Phenotypic plasticity 1. development 2. evolution The short version: 3. epigenetics 4. canalization 5. cryptic genetic variation / capacitance phenotype depends on the environment 6. genetic assimilation 7. evolvability 8. preadaptation 9. phenotypic plasticity 10. epistasis 11. modularity 12. developmental bias / constraint Phenotypic plasticity: long version Phenotypic plasticity • Gradual response to environmental gradient OR “When I use a word, it means just what I discrete switching between types choose it to mean – neither more nor less” • Fixed traits (eg size after metamorphosis) OR Humpty Dumpty labile traits (eg behavioral) • Change in the phenotypic mean (reaction norm) OR change in the phenotypic variance “When I make a word do a lot of work like that, • Response to a directional environmental shift OR I always pay it extra” response to residual environmental “noise” Humpty Dumpty • Adaptive, evolved response OR side effect of basic physiology How does phenotypic plasticity trait Spectrum adaptive tie in to other concepts? phenotypic plasticity Some contradictory (?) ideas: random preadapted • Genetic assimilation of plastic trait is loss not gain mutation cryptic variation of complexity, so genetic assimilation is irrelevant • Random variation unlikely to be adaptive to the evolution of novelty (Williams) • Very deleterious stuff weeded out • Canalization is the opposite of phenotypic • Revealed variation more likely to be adaptive plasticity (Goggy) • Trait needed briefly, then lost • Revelation of cryptic genetic variation in response • Easier to find it again in cryptic variation to stress is one kind of phenotypic plasticity • Frequent environmental switching ⇒ evolution of adaptive phenotypic plasticity (or reaction norm) • Environment stabilizes ⇒ genetic assimilation with loss of complexity 1 10. Epistasis 11. Modularity • General definition: the effect of an allele at one • Pleiotropic effects locus depends on alleles at other loci within but not between • Population genetics definition: departure from modules multiplicative effects, where fitness is the • Evolvability: one module product of independent fitness components can then evolve without • Quantitative genetics definition: departure from disturbing the others additive effects, seen as interaction terms under • Modularity can arise by a linear regression model either parcellation or • Nonepistatic relationships under one definition integration show up as epistatic under the other Wagner & Alternberg 1996 Sting rays descended from sharks 12. Developmental bias / constraint Sharks were • Evolution can only work with variants that appear already a bit flattened • Developmental constraints can stop useful things Rays and evolving skates lie on their bellies • Look at fish living at sea bottom as an example of a developmental constraint that wasn’t quite strong enough Ancestor of flounders had flattish sides Flounders, plaice and sole lie on their side During development, eyes must twist around to get to same side of body 2.

Copy Link

Recommended publications

Modularity As a Concept Modern Ideas About Mental Modularity Typically Use Fodor (1983) As a Key Touchstone
COGNITIVE PROCESSING International Quarterly of Cognitive Science Mental modularity, metaphors, and the marriage of evolutionary and cognitive sciences GARY L. BRASE University of Missouri – Columbia Abstract - As evolutionary approaches in the behavioral sciences become increasingly prominent, issues arising from the proposition that the mind is a collection of modular adaptations (the multi-modular mind thesis) become even more pressing. One purpose of this paper is to help clarify some valid issues raised by this thesis and clarify why other issues are not as critical. An aspect of the cognitive sciences that appears to both promote and impair progress on this issue (in different ways) is the use of metaphors for understand- ing the mind. Utilizing different metaphors can yield different perspectives and advancement in our understanding of the nature of the human mind. A second purpose of this paper is to outline the kindred natures of cognitive science and evolutionary psychology, both of which cut across traditional academic divisions and engage in functional analyses of problems. Key words: Evolutionary Theory, Cognitive Science, Modularity, Metaphors Evolutionary approaches in the behavioral sciences have begun to influence a wide range of fields, from cognitive neuroscience (e.g., Gazzaniga, 1998), to clini- cal psychology (e.g., Baron-Cohen, 1997; McGuire and Troisi, 1998), to literary theory (e.g., Carroll, 1999). At the same time, however, there are ongoing debates about the details of what exactly an evolutionary approach – often called evolu- tionary psychology— entails (Holcomb, 2001). Some of these debates are based on confusions of terminology, implicit arguments, or misunderstandings – things that can in principle be resolved by clarifying current ideas.
[Show full text]
From Developmental Constraint to Evolvability
From Developmental Constraint to Evolvability How Concepts Figure in Explanation and Disciplinary Identity Ingo Brigandt Department of Philosophy, University of Alberta 2-40 Assiniboia Hall, Edmonton, AB T6G2E7, Canada [email protected] Abstract The concept of developmental constraint was at the heart of develop- mental approaches to evolution of the 1980s. While this idea was widely used to criticize neo-Darwinian evolutionary theory, critique does not yield an alternative framework that offers evolutionary explanations. In current Evo-devo the concept of constraint is of minor importance, whereas notions as evolvability are at the center of attention. The latter clearly defines an explanatory agenda for evolution- ary research, so that one could view the historical shift from ‘developmental con- straint’ towards ‘evolvability’ as the move from a concept that is a mere tool of criticism to a concept that establishes a positive explanatory project. However, by taking a look at how the concept of constraint was employed in the 1980s, I argue that developmental constraint was not just seen as restricting possibilities (‘con- straining’), but also as facilitating morphological change in several ways. Ac- counting for macroevolutionary transformation and the origin of novel form was an aim of these developmental approaches to evolution. Thus, the concept of de- velopmental constraint was part of a positive explanatory agenda long before the advent of Evo-devo as a genuine scientific discipline. In the 1980s, despite the lack of a clear disciplinary identity, this concept coordinated research among pale- ontologists, morphologists, and developmentally inclined evolutionary biologists. I discuss the different functions that scientific concepts can have, highlighting that instead of classifying or explaining natural phenomena, concepts such as ‘devel- opmental constraint’ and ‘evolvability’ are more important in setting explanatory agendas so as to provide intellectual coherence to scientific approaches.
[Show full text]
Transformations of Lamarckism Vienna Series in Theoretical Biology Gerd B
Transformations of Lamarckism Vienna Series in Theoretical Biology Gerd B. M ü ller, G ü nter P. Wagner, and Werner Callebaut, editors The Evolution of Cognition , edited by Cecilia Heyes and Ludwig Huber, 2000 Origination of Organismal Form: Beyond the Gene in Development and Evolutionary Biology , edited by Gerd B. M ü ller and Stuart A. Newman, 2003 Environment, Development, and Evolution: Toward a Synthesis , edited by Brian K. Hall, Roy D. Pearson, and Gerd B. M ü ller, 2004 Evolution of Communication Systems: A Comparative Approach , edited by D. Kimbrough Oller and Ulrike Griebel, 2004 Modularity: Understanding the Development and Evolution of Natural Complex Systems , edited by Werner Callebaut and Diego Rasskin-Gutman, 2005 Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity on the Gradualist Framework of Evolution , by Richard A. Watson, 2006 Biological Emergences: Evolution by Natural Experiment , by Robert G. B. Reid, 2007 Modeling Biology: Structure, Behaviors, Evolution , edited by Manfred D. Laubichler and Gerd B. M ü ller, 2007 Evolution of Communicative Flexibility: Complexity, Creativity, and Adaptability in Human and Animal Communication , edited by Kimbrough D. Oller and Ulrike Griebel, 2008 Functions in Biological and Artiﬁ cial Worlds: Comparative Philosophical Perspectives , edited by Ulrich Krohs and Peter Kroes, 2009 Cognitive Biology: Evolutionary and Developmental Perspectives on Mind, Brain, and Behavior , edited by Luca Tommasi, Mary A. Peterson, and Lynn Nadel, 2009 Innovation in Cultural Systems: Contributions from Evolutionary Anthropology , edited by Michael J. O ’ Brien and Stephen J. Shennan, 2010 The Major Transitions in Evolution Revisited , edited by Brett Calcott and Kim Sterelny, 2011 Transformations of Lamarckism: From Subtle Fluids to Molecular Biology , edited by Snait B.
[Show full text]
Why Call It Developmental Bias When It Is Just Development? Isaac Salazar-Ciudad1,2,3
Salazar-Ciudad Biology Direct (2021) 16:3 https://doi.org/10.1186/s13062-020-00289-w OPINION Open Access Why call it developmental bias when it is just development? Isaac Salazar-Ciudad1,2,3 Abstract The concept of developmental constraints has been central to understand the role of development in morphological evolution. Developmental constraints are classically defined as biases imposed by development on the distribution of morphological variation. This opinion article argues that the concepts of developmental constraints and developmental biases do not accurately represent the role of development in evolution. The concept of developmental constraints was coined to oppose the view that natural selection is all-capable and to highlight the importance of development for understanding evolution. In the modern synthesis, natural selection was seen as the main factor determining the direction of morphological evolution. For that to be the case, morphological variation needs to be isotropic (i.e. equally possible in all directions). The proponents of the developmental constraint concept argued that development makes that some morphological variation is more likely than other (i.e. variation is not isotropic), and that, thus, development constraints evolution by precluding natural selection from being all-capable. This article adds to the idea that development is not compatible with the isotropic expectation by arguing that, in fact, it could not be otherwise: there is no actual reason to expect that development could lead to isotropic morphological variation. It is then argued that, since the isotropic expectation is untenable, the role of development in evolution should not be understood as a departure from such an expectation.
[Show full text]
The Impact of Component Modularity on Design Evolution: Evidence from the Software Industry
08-038 The Impact of Component Modularity on Design Evolution: Evidence from the Software Industry Alan MacCormack John Rusnak Carliss Baldwin Copyright © 2007 by Alan MacCormack, John Rusnak, and Carliss Baldwin. Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may not be reproduced without permission of the copyright holder. Copies of working papers are available from the author. Abstract Much academic work asserts a relationship between the design of a complex system and the manner in which this system evolves over time. In particular, designs which are modular in nature are argued to be more “evolvable,” in that these designs facilitate making future adaptations, the nature of which do not have to be specified in advance. In essence, modularity creates “option value” with respect to new and improved designs, which is particularly important when a system must meet uncertain future demands. Despite the conceptual appeal of this research, empirical work exploring the relationship between modularity and evolution has had limited success. Three major challenges persist: first, it is difficult to measure modularity in a robust and repeatable fashion; second, modularity is a property of individual components, not systems as a whole, hence we must examine these dynamics at the microstructure level; and third, evolution is a temporal phenomenon, in that the conditions at time t affect the nature of the design at time t+1, hence exploring this phenomenon requires longitudinal data. In this paper, we tackle these challenges by analyzing the evolution of a successful commercial software product over its entire lifetime, comprising six major “releases.” In particular, we develop measures of modularity at the component level, and use these to predict patterns of evolution between successive versions of the design.
[Show full text]
Task-Dependent Evolution of Modularity in Neural Networks1
Task-dependent evolution of modularity in neural networks1 Michael Husk¨ en, Christian Igel, and Marc Toussaint Institut fur¨ Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum, Germany Telephone: +49 234 32 25558, Fax: +49 234 32 14209 fhuesken,igel,[email protected] Connection Science Vol 14, No 3, 2002, p. 219-229 Abstract. There exist many ideas and assumptions about the development and meaning of modu- larity in biological and technical neural systems. We empirically study the evolution of connectionist models in the context of modular problems. For this purpose, we define quantitative measures for the degree of modularity and monitor them during evolutionary processes under different constraints. It turns out that the modularity of the problem is reflected by the architecture of adapted systems, although learning can counterbalance some imperfection of the architecture. The demand for fast learning systems increases the selective pressure towards modularity. 1 Introduction The performance of biological as well as technical neural systems crucially depends on their ar- chitectures. In case of a feed-forward neural network (NN), architecture may be defined as the underlying graph constituting the number of neurons and the way these neurons are connected. Particularly one property of architectures, modularity, has raised a lot of interest among researchers dealing with biological and technical aspects of neural computation. It appears to be obvious to emphasise modularity in neural systems because the vertebrate brain is highly modular both in an anatomical and in a functional sense. It is important to stress that there are different concepts of modules. `When a neuroscientist uses the word \module", s/he is usually referring to the fact that brains are structured, with cells, columns, layers, and regions which divide up the labour of information processing in various ways' 1This paper is a revised and extended version of the GECCO 2001 Late-Breaking Paper by Husk¨ en, Igel, & Toussaint (2001).
[Show full text]
Modularity in Animal Development and Evolution: Elements of a Conceptual Framework for Evodevo
JOURNAL OF EXPERIMENTAL ZOOLOGY (MOL DEV EVOL) 285:307–325 (1999) Modularity in Animal Development and Evolution: Elements of a Conceptual Framework for EvoDevo GEORGE VON DASSOW*AND ED MUNRO Department of Zoology, University of Washington, Seattle, Washington 98195-1800 ABSTRACT For at least a century biologists have been talking, mostly in a black-box sense, about developmental mechanisms. Only recently have biologists succeeded broadly in fishing out the contents of these black boxes. Unfortunately the view from inside the black box is almost as obscure as that from without, and developmental biologists increasingly confront the need to synthesize known facts about developmental phenomena into mechanistic descriptions of com- plex systems. To evolutionary biologists, the emerging understanding of developmental mecha- nisms is an opportunity to understand the origins of variation not just in the selective milieu but also in the variability of the developmental process, the substrate for morphological change. Ultimately, evolutionary developmental biology (EvoDevo) expects to articulate how the diversity of organic form results from adaptive variation in development. This ambition demands a shift in the way biologists describe causality, and the central problem of EvoDevo is to understand how the architecture of development confers evolvability. We argue in this essay that it makes little sense to think of this question in terms of individual gene function or isolated morphometrics, but rather in terms of higher-order modules such as gene networks and homologous characters. We outline the conceptual challenges raised by this shift in perspective, then present a selection of case studies we believe to be paradigmatic for how biologists think about modularity in devel- opment and evolution.
[Show full text]
The Emergence of Modularity in Biological Systems
The Emergence of Modularity in Biological Systems Zhenyu Wang Dec. 2007 Abstract: Modularity is a ubiquitous phenomenon in various biological systems, both in genotype and in phenotype. Biological modules, which consist of components relatively independent in functions as well as in morphologies, have facilitated daily performance of biological systems and their long time evolution in history. How did modularity emerge? A mechanism with horizontal gene transfer and a biological version of spin-glass model is addressed here. When the spontaneous symmetry is broken in an evolving environment, here comes modularity. Further improvement is also discussed at the end. 1 Introduction Modularity is an important concept with broad applications in computer science, especially in programming. A module, usually composed of various components, is a relatively independent entity in its operation with respect to other entities in the whole system. For a programmer, the employment of a module will increase the efficiency of coding, decrease the potential of errors, enhance the stability of the operation and facilitate the further improvement of the program. Coincidently, the nature thinks the same in its programming life: various biological systems are also in a modular fashion! Let us take the model organism fruit fly Drosophila melanogaster as an example to sip the ubiquitous modularity in biological systems. In view of morphology, Drosophila is a typical modular insect, whose adult is composed of an anterior head, followed by three thoracic segments, and eight abdominal segments at posterior[1](Fig. 1). Such a kind of segmentation is induced by a cascade of transcription factors in the embryo. First, maternal effect genes which are expressed in the mother’s ovary cells distribute cytoplasmic determinants unevenly in fertilized eggs, which thus determines anterior-posterior and dorsal-ventral axis.
[Show full text]
Getting to Evo-Devo: Concepts and Challenges for Students Learning Evolutionary Developmental Biology Anna Hiatt
Bryn Mawr College Scholarship, Research, and Creative Work at Bryn Mawr College Biology Faculty Research and Scholarship Biology 2013 Getting to Evo-Devo: Concepts and Challenges for Students Learning Evolutionary Developmental Biology Anna Hiatt Gregory K. Davis Bryn Mawr College, [email protected] Caleb Trujillo Mark Terry Donald P. French See next page for additional authors Let us know how access to this document benefits ouy . Follow this and additional works at: http://repository.brynmawr.edu/bio_pubs Part of the Biology Commons Custom Citation Hiatt A, Davis GK, Trujillo C, Terry M, French DP, Price RM and Perez KE . "Getting to evo-devo: concepts and challenges for students learning evolutionary developmental biology". CBE-Life Sciences Education 12 (2013): 494-508. This paper is posted at Scholarship, Research, and Creative Work at Bryn Mawr College. http://repository.brynmawr.edu/bio_pubs/10 For more information, please contact [email protected]. Authors Anna Hiatt, Gregory K. Davis, Caleb Trujillo, Mark Terry, Donald P. French, Rebecca M. Price, and Kathryn E. Perez This article is available at Scholarship, Research, and Creative Work at Bryn Mawr College: http://repository.brynmawr.edu/ bio_pubs/10 CBE—Life Sciences Education Vol. 12, 494–508, Fall 2013 Article Getting to Evo-Devo: Concepts and Challenges for Students Learning Evolutionary Developmental Biology Anna Hiatt,*† Gregory K. Davis,‡ Caleb Trujillo,§ Mark Terry, Donald P. French,* Rebecca M. Price,¶ and Kathryn E. Perez** *Department of Zoology, Oklahoma
[Show full text]
Evolution of Networks for Body Plan Patterning; Interplay of Modularity, Robustness and Evolvability
Evolution of Networks for Body Plan Patterning; Interplay of Modularity, Robustness and Evolvability Kirsten H. ten Tusscher1,2*, Paulien Hogeweg1 1 Theoretical Biology and Bioinformatics Group, Department of Biology, Faculty of Science, Utrecht University, Utrecht, The Netherlands, 2 Scientific Computing, Simula Research Laboratory, Oslo, Norway Abstract A major goal of evolutionary developmental biology (evo-devo) is to understand how multicellular body plans of increasing complexity have evolved, and how the corresponding developmental programs are genetically encoded. It has been repeatedly argued that key to the evolution of increased body plan complexity is the modularity of the underlying developmental gene regulatory networks (GRNs). This modularity is considered essential for network robustness and evolvability. In our opinion, these ideas, appealing as they may sound, have not been sufficiently tested. Here we use computer simulations to study the evolution of GRNs’ underlying body plan patterning. We select for body plan segmentation and differentiation, as these are considered to be major innovations in metazoan evolution. To allow modular networks to evolve, we independently select for segmentation and differentiation. We study both the occurrence and relation of robustness, evolvability and modularity of evolved networks. Interestingly, we observed two distinct evolutionary strategies to evolve a segmented, differentiated body plan. In the first strategy, first segments and then differentiation domains evolve (SF strategy). In the second scenario segments and domains evolve simultaneously (SS strategy). We demonstrate that under indirect selection for robustness the SF strategy becomes dominant. In addition, as a byproduct of this larger robustness, the SF strategy is also more evolvable. Finally, using a combined functional and architectural approach, we determine network modularity.
[Show full text]
Modularity in Signaling Systems
Modularity in Signaling Systems Domitilla Del Vecchio Department of Mechanical Engineering, Laboratory for Information and Decision Systems (LIDS), MIT, 77 Massachusetts Ave, Cambridge, MA 02139 E-mail: [email protected] Modularity in Signaling Systems 2 Abstract. Modularity is a property by which the behavior of a system does not change upon interconnection. It is crucial for understanding the behavior of a complex system from the behavior of the composing subsystems. Whether modularity holds in biology is an intriguing and largely debated question. In this paper, we discuss this question taking a control systems theory view and focusing on signaling systems. In particular, we argue that, despite signaling systems are constituted of structural modules, such as covalent modification cycles, modularity does not hold in general. As in any engineering system, impedance-like effects, called retroactivity, appear at interconnections and alter the behavior of connected modules. We further argue that while signaling systems have evolved sophisticated ways to counter-act retroactivity and enforce modularity, retroactivity may also be exploited to finely control the information processing of signaling pathways. Testable predictions and experimental evidence are discussed with their implications. 1. Introduction The idea that modularity is a general principle of biological organization and that signaling systems, in particular, are modularly assembled has been considered and discussed by several researchers [1,10,21]. In particular, a modular systems approach to understanding information transfer in signaling networks has been proposed as a way to defeat the complexity of networks [4,20,24,25]. For example, signaling pathways involved in caspase activation have been studied through a modular approach to determine the key contributors in triggering apoptosis [9].
[Show full text]
Varieties of Modules: Kinds, Levels, Origins, and Behaviors RASMUS G
116JOURNAL OF EXPERIMENTAL R.G. WINTHER ZOOLOGY (MOL DEV EVOL) 291:116–129 (2001) Varieties of Modules: Kinds, Levels, Origins, and Behaviors RASMUS G. WINTHER* Department of History and Philosophy of Science, and Department of Biology, Indiana University, Bloomington, Indiana 47405 ABSTRACT This article began as a review of a conference, organized by Gerhard Schlosser, entitled “Modularity in Development and Evolution.” The conference was held at, and sponsored by, the Hanse Wissenschaftskolleg in Delmenhorst, Germany in May, 2000. The article subse- quently metamorphosed into a literature and concept review as well as an analysis of the differ- ences in current perspectives on modularity. Consequently, I refer to general aspects of the conference but do not review particular presentations. I divide modules into three kinds: struc- tural, developmental, and physiological. Every module fulfills none, one, or multiple functional roles. Two further orthogonal distinctions are important in this context: module-kinds versus mod- ule-variants-of-a-kind and reproducer versus nonreproducer modules. I review criteria for indi- viduation of modules and mechanisms for the phylogenetic origin of modularity. I discuss conceptual and methodological differences between developmental and evolutionary biologists, in particular the difference between integration and competition perspectives on individualization and modular behavior. The variety in views regarding modularity presents challenges that require resolution in order to attain a comprehensive,
[Show full text]